In wireless communication systems, transmitted signals are corrupted by fading as well as noise. The receiver can benefit from the estimates of fading channels to detect the transmitted symbols. However, in practical wireless systems channel information cannot be estimated perfectly at the receiver. Therefore, it is crucial to examine the effect of channel estimation error on the structure and performance of the receivers. In the first part of the thesis, we study single-user systems with single-antenna reception over fading channels in the presence of Gaussian-distributed channel estimation error. By using the statistical information of the channel estimation error, we will derive the structure of maximum-likelihood receivers for a number of different modulation formats and then analyze their performance over fading channels. In the second part of the thesis, we consider the uplink of multi-user wireless systems with multi-antenna reception. For conventional diversity combining techniques such as maximal ratio combining and optimum combining we analyze the performance degradation due to imperfect channel estimates in the presence of multiple interfering users for several fading channels. By investigating the probability density function of the output signal-to-interference ratio, we will derive analytical expressions for several performance measures such as the average signal-to-interference ratio, outage probability and average bit-error probability. These expressions quantify performance degradation due to channel estimation error.

In wireless communication systems, transmitted signals are corrupted by fading as well as noise. The receiver can benefit from the estimates of fading channels to detect the transmitted symbols. However, in practical wireless systems channel information cannot be estimated perfectly at the receiver. Therefore, it is crucial to examine the effect of channel estimation error on the structure and performance of the receivers. In the first part of the thesis, we study single-user systems with single-antenna reception over fading channels in the presence of Gaussian-distributed channel estimation error. By using the statistical information of the channel estimation error, we will derive the structure of maximum-likelihood receivers for a number of different modulation formats and then analyze their performance over fading channels. In the second part of the thesis, we consider the uplink of multi-user wireless systems with multi-antenna reception. For conventional diversity combining techniques such as maximal ratio combining and optimum combining we analyze the performance degradation due to imperfect channel estimates in the presence of multiple interfering users for several fading channels. By investigating the probability density function of the output signal-to-interference ratio, we will derive analytical expressions for several performance measures such as the average signal-to-interference ratio, outage probability and average bit-error probability. These expressions quantify performance degradation due to channel estimation error.

Thesis (Ph. D.)--University of California, San Diego, 2008. / Title from first page of PDF file (viewed June 16, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 158-164).

This dissertation considers three related problems in distributed transmission and reception networks. Generally speaking, these types of networks have a transmit cluster with one or more transmit nodes and a receive cluster with one or more receive nodes. Nodes within a given cluster can communicate with each other using a wired or wireless local area network (LAN/WLAN). The overarching goal in this setting is typically to increase the efficiency of communication between the transmit and receive clusters through techniques such as distributed transmit beamforming, distributed reception, or other distributed versions of multi-input multi-output (MIMO) communication. More recently, the problem of wireless power transfer has also been considered in this setting. The first problem considered by this dissertation relates to distributed reception in a setting with a single transmit node and multiple receive nodes. Since exchanging lightly quantized versions of in-phase and quadrature samples results in high throughput requirements on the receive LAN/WLAN, previous work has considered an approach where nodes exchange hard decisions, along with channel magnitudes, to facilitate combining similar to an ideal receive beamformer. It has been shown that this approach leads to a small loss in SNR performance, with large reductions in required LAN/WLAN throughput. A shortcoming of this work, however, is that all of the prior work has assumed that each receive node has a perfect estimation of its channel to the transmitter. To address this shortcoming, the first part of this dissertation investigates the effect of channel estimation error on the SNR performance of distributed reception. Analytical expressions for these effects are obtained for two different modulation schemes, M-PSK and M2-QAM. The analysis shows the somewhat surprising result that channel estimation error causes the same amount of performance degradation in ideal beamforming and pseudo-beamforming systems despite the fact that the channel estimation errors manifests themselves quite differently in both systems. The second problem considered in this dissertation is related to oscillator stability and phase noise modeling. In distributed transmission systems with multiple transmitters in the transmit cluster, synchronization requirements are typically very strict, e.g., on the order of one picosecond, to maintain radio frequency phase alignment across transmitters. Therefore, being able to accurately model the behavior of the oscillators and their phase noise responses is of high importance. Previous approaches have typically relied on a two-state model, but this model is often not sufficiently rich to model low-cost oscillators. This dissertation develops a new three-state oscillator model and a method for estimating the parameters of this model from experimental data. Experimental results show that the proposed model provides up to 3 dB improvement in mean squared error (MSE) performance with respect to a two-state model. The last part of this work is dedicated to the problem of wireless power transfer in a setting with multiple nodes in the transmit cluster and multiple nodes in the receive cluster. The problem is to align the phases of the transmitters to achieve a certain power distribution across the nodes in the receive cluster. To find optimum transmit phases, we consider a iterative approach, similar to the prior work on one-bit feedback for distributed beamforming, in which each receive node sends a one-bit feedback to the transmit cluster indicating if the received power in that time slot for that node is increased. The transmitters then update their phases based on the feedback. What makes this problem particularly interesting is that, unlike the prior work on one-bit feedback for distributed beamforming, this is a multi-objective optimization problem where not every receive node can receive maximum power from the transmit array. Three different phase update decision rules, each based on the one-bit feedback signals, are analyzed. The effect of array sparsity is also investigated in this setting.

This diploma thesis describes the Bornhuetter-Ferguson method, which is used to calculate the IBNR reserve. It is divided into deterministic and stochastic parts. The deterministic part deals with the derivation of development pattern and ultimate loss amount, which are needed to calculate the reserve. The stochastic part deals with reserve estimation error and prediction error. The calculation results of the reserve estimate and its error are compared with the results of the chain ladder method. The last chapter deals with the problematic areas of the described method.

This thesis comprises four papers concerning risk prediction. Paper [I] suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks. Using daily data 2000-2006 for the Baltic state stock exchanges and that of Moscow we find recursive structures with Riga directly depending in returns on Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities both Riga and Vilnius depend on Tallinn. In addition, we find evidence of asymmetric effects of shocks arising in Moscow and in the Baltic states on both returns and volatilities. Paper [II] argues that the estimation error in Value at Risk predictors gives rise to underestimation of portfolio risk. A simple correction is proposed and in an empirical illustration it is found to be economically relevant. Paper [III] studies some approximation approaches to computing the Value at Risk and the Expected Shortfall for multiple period asset re- turns. Based on the result of a simulation experiment we conclude that among the approaches studied the one based on assuming a skewed t dis- tribution for the multiple period returns and that based on simulations were the best. We also found that the uncertainty due to the estimation error can be quite accurately estimated employing the delta method. In an empirical illustration we computed five day Value at Risk's for the S&P 500 index. The approaches performed about equally well. Paper [IV] argues that the practise used in the valuation of the port- folio is important for the calculation of the Value at Risk. In particular, when liquidating a large portfolio the seller may not face horizontal de- mandcurves. We propose a partially new approach for incorporating this fact in the Value at Risk and in an empirical illustration we compare it to a competing approach. We find substantial differences.

In this paper, we identify sufficient conditions for Lyapunov Mean Square Stability (LMSS) of a contention-based network of first-order systems, with state-based schedulers. The stability analysis helps us to choose policies for adapting the scheduler threshold to the delay from the network and scheduler. We show that three scheduling laws can result in LMSS: constant-probability laws and additively increasing or decreasing probability laws. Our results counter the notions that increasing probability scheduling laws alone can guarantee stability of the closed-loop system, or that decreasing probability scheduling laws are required to mitigate congestion in the network. / <p>QC 20130116</p>

Cooperative communications have received much attention recently, due to its ability to attain cooperation diversity. But when two nodes communicate via relays, it is difficult to get the perfect channel information, so relays must estimate their forward channel and backward channel in order to amplify the data to the destination. We investigate the effect of channel estimation error, and design the LMMSE estimator to estimate the channels, and also we consider the multi-relays to assist the whole system for training and data transmission. We propose the SNR gap ratio, outage probability, and the BER simulations for the analysis. Simulation shows that when using multi-relays, it can mitigate the effect of channel estimation errors in all of the amplify-and-forward (AF) scenarios.

Recently, interference alignment (IA) has emerged as a promising technique to effectively mitigate interference in wireless communication systems. It has also evolved as a powerful technique to achieve the optimal degrees of freedom of interference channel. IA can be constructed in many domains such as space, time, frequency and codes. Currently, most researches on developing IA assume that channel state information (CSI) is well-known at the transceiver. However, in practice, perfect CSI at the transceiver can¡¦t be obtained due to many factors such as channel estimation error, quantization error, and feedback error. Under our investigation, the performance of IA is very sensitive to imperfect CSI. Therefore, this thesis proposes a spatial domain IA scheme for the three-user multiple-input multiple-output (MIMO) downlink interference channels, and analyzes the effect of channel estimation errors by modeling the estimation error as independent complex Gaussian random variables. The approximated bit error rate (BER) for the system with MIMO Zero-Forcing equalizer using IA is derived.

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